Given billions of years of evolution, nature has come up with proteins that can perform staggering feats of chemistry—in some cases, a few grams of protein would be enough to outperform a large industrial facility. Understanding how these proteins work can have applications in areas from energy to industry to medicine.

But that understanding has been hard to come by. It's possible to take static pictures of what the enzymes look like when they are stuck in a crystal, but that only provides a limited picture. The enzymes actually work in a dynamic environment, undergo physical changes, and catalyze reactions that involve the rapid shuffling of chemical bonds and transfer of electrons.

This year's Nobel Prize in Chemistry recognizes three researchers who helped bring dynamism to our study of large molecules like proteins. Martin Karplus, Michael Levitt, and Arieh Warshel started studying how to simulate the activity that goes on inside a protein back in the 1970s, when computing power was extremely hard to come by. The descendants of the methods they developed are still in use today, even as our computational models have grown ever larger and more sophisticated.

To provide some sense of the scale of the problem, you can admire carbonic anhydrase, the fastest enzyme in the world. Although it only catalyzes a simple chemical reaction, some versions of the enzyme can turn over 1,000,000 reactions every second. And each one of those involves a bit of flexing in the enzyme itself, chemicals moving in and out of its active site, and a rearrangement of chemical bonds.

Yet for most of these catalysts, all we have is a static picture. Techniques like X-ray crystallography can tell us where all the atoms in a protein typically reside, but only if those atoms sit still for their portrait. The other major technique for determining structures, NMR imaging, works when the proteins are in solution, but it only provides an indication of what the atoms are doing on average.

So how do we get a glimpse of the dynamic world where enzymes really operate if all we have are single snapshots? Given the position of the atoms, it should be possible to use fundamental physics to tell us what they would do as they interact with one another while bumping into water and other molecules in their environment. But that requires some pretty hefty computations, and computers aren't always up to the task. This situation was an even more significant problem back when the prize winners started their work in the 1970s.

Early computers had the ability to model molecules as classical systems—the ball-and-stick view of atoms and their bonds, which did allow them to flex and bounce off each other. But each bounce involves the interactions of the outermost shells of electrons held by these atoms, and their behavior is governed by quantum mechanics. This is even more true for the reactions catalyzed by proteins, which generally involve the transfer of electrons and/or protons. The problem is that describing the quantum behavior of these systems adds a tremendous amount of computational complexity, enough to overwhelm the computers of the time.

The researchers are being honored for their development of a hybrid quantum-classical model of molecular behavior. A key event in this process came when Arieh Warshel (now at USC) spent time in the lab of Martin Karplus, then and still at Harvard. Warshel had experience with classical modeling, while Karplus brought expertise in the quantum behavior of molecules. Together, they built a model of a large organic molecule: retinal, the molecule that absorbs photons within the photoreceptors that provide us with vision. Retinal has a complicated structure that includes rings and alternating double and single bonds, which allows some of the outermost electrons to become delocalized and orbit the molecule as a whole.

Warshel and Karplus' model treated most of the molecule using easy-to-calculate classical mechanics. But the delocalized electrons were given a quantum treatment. The combination allowed the calculations to be simple enough that they would run on the computers available at that time but still capture the most important features of a dynamic molecule.

By the mid-'70s, Warshel was collaborating with Michael Levitt (now at Stanford). By improving and generalizing the techniques used on retinal and taking advantage of the growing power of computers, the two were able to create a hybrid classical-quantum model that was able to describe the behavior inside a protein's active site. For proteins, quantum calculations are used for the area near where the chemical reactions take place. The parts of the protein distant from that have atoms, or even clusters of atoms, that are treated as single classical objects.

As computing power grows, more and more of the molecules are able to be given a sophisticated treatment, and further details like the presence of water molecules can be added to the model. But the basic approach pioneered by the researchers being honored today is still widely in use.

17 Reader Comments

Dr. Warshel is a professor at USC, not UCLA. USC is also home to another chemistry Nobel laureate, George Olah, who won in 1994 for his work on carbocations. I graduated with my Ph.D. in Chemistry from there in 2005. I remember passing by Warshel's lab many times and it being filled with workstations crunching away at model systems.

Because evolution is what produced the kinds of extraordinary reaction efficiencies that are one of the most important reasons for studying these molecules. Evolution is the foundation that all modern biology is based on, so it is pretty hard to even find a topic in biology that it isn't relevant to.

Tearfang: It is much faster than QM and infinitely more accurate than MM for anything meaningful ;-)

Typically it is not "true" QM that is currently used but rather semi-empirical techniques where data is used to fit the behavior of the QM model to experimental results or higher levels of theory. True "ab initio" (from the beginning - based on physical constants) QM methods are much slower, we're working on it though!

Are computers these days powerful enough to calculate full QM on a molecule like Retinal, or chlorophyll, or any interesting enzyme, and fully reproduce it's known behaviours and reactions? Has anyone tried?

Are computers these days powerful enough to calculate full QM on a molecule like Retinal, or chlorophyll, or any interesting enzyme, and fully reproduce it's known behaviours and reactions? Has anyone tried?

Currently, to treat molecular systems with order-of 1000 atoms, a density functional approach (for approximate solution of the time-independent electronic Schroedinger equation describing the electrons/orbitals of the molecular system of interest) is the only practical one. Other purely quantum approximations are too computationally expensive. However, density functional approaches have typically been insufficiently accurate for systems where weak electronic interactions were important/dominant (such as is the case for biomolecules). Recently, work is being done to address such issues. See, e.g., this article by Kolb and Thonhauser from 2012.

Are computers these days powerful enough to calculate full QM on a molecule like Retinal, or chlorophyll, or any interesting enzyme, and fully reproduce it's known behaviours and reactions? Has anyone tried?

Currently, to treat molecular systems with order-of 1000 atoms, a density functional approach (for approximate solution of the time-independent electronic Schroedinger equation describing the electrons/orbitals of the molecular system of interest) is the only practical one. Other purely quantum approximations are too computationally expensive. However, density functional approaches have typically been insufficiently accurate for systems where weak electronic interactions were important/dominant (such as is the case for biomolecules). Recently, work is being done to address such issues. See, e.g., this article by Kolb and Thonhauser from 2012.

Very interesting, thanks! I hope that further work on yet more accurate and efficient modeling one day results in being able to simulate a very small bacterium (the smallest known) right down to the atom. Hopefully by the end of the 21st century? Such capability would be a great way to test synthetic organisms and nano-machines/particles.

EDIT: Here's an idea for the future: setup up a really nice simulation of chlorophyll, then run a genetic algorithm on it to increase it's efficiency as much as possible. Take the resultant protein and aim to synthesize an organism that expresses it. Take similar steps to develop other molecules for a pathway that efficiently turns CO2, Sunlight and water into bio-butanol. This is followed by lots of profit and a carbon neutral liquid fuel.

Wrong twice: I didn't realize biology research and QM had intersected, and thought protein folding was well understood. For other lay persons, I quote from the paper mentioned in the comments:

The scientific disciplines (e.g. biology, chemistry, physics) once stood well separated from each other, with practitioners from each approaching different questions in different ways. These divisions are be- ginning to blur, however, as answers to questions from one field increasingly require techniques and knowledge built up in another. There is evidence of this effect in the increasing need for interdisci- plinary collaborations to solve problems arising in distinct fields. A particularly poignant example of this blurring of lines is the field of molecular biology, where researchers try to build an understanding of biological systems starting at the molecular level.

Wrong twice: I didn't realize biology research and QM had intersected, and thought protein folding was well understood.

Generally, molecular modelling and QM/MM methods are not applied to protein folding, since there are much more efficient techniques for characterizing general protein structure. The techniques that won this prize are applied after you have a structure determined, to 'watch it in action' - to see how that structure actually gives rise to function, and to possibly manipulate that function.

Regarding Fun expt to try in the kitchen: drop a small piece of raw liver into dilute hydrogen peroxide. Observe the mayhem

Couple of questions about this, I do love a good dose of mayhem, but I'd quite like to know:

How easy will hydrogen peroxide be to obtain in the UK?Will I end up on some kind of list if I try to get hold of it?Will my wife kill me if I actually do this in the house?

Cheers

The mayhem quotient for this is not excessive. As long as your wife doesn't mind the smell of raw liver then it shouldn't adversely impact your marriage.

Hydrogen peroxide is a pretty standard household chemical and getting some from either a chemical supplier or via some household products should be pretty straightforward. It's used as a bleach for all kinds of things, from hair (hence the term peroxide blond) to floors.

[edit] If you are getting it from a chemical supplier make sure you get the dilute stuff though.

Wrong twice: I didn't realize biology research and QM had intersected, and thought protein folding was well understood.

Generally, molecular modelling and QM/MM methods are not applied to protein folding, since there are much more efficient techniques for characterizing general protein structure. The techniques that won this prize are applied after you have a structure determined, to 'watch it in action' - to see how that structure actually gives rise to function, and to possibly manipulate that function.

Yes, correct, the protein folding problem is not amenable to a QM (or semi-empirical) or MM approach. There are far too many degrees of freedom (3*N - 6, where N = number of atoms) to attempt to explore the full dimensionality of that hypersurface and try to identify all important local minima (let alone the global minimum) based on a computed wave function or based on total energy computed from an MM force field.

What one can do, however, is - given a know protein structure (from, e.g., x-ray crystallography) - use methods of QM/MM or some hybrid approach to study the energy and structure of interactions at important binding site(s) of the protein with goals of drug discovery or catalysis or other topics or interest involving the function of protein binding sites.